app.py

from keras.models import load_model  # TensorFlow is required for Keras to work
from PIL import Image, ImageOps  # Install pillow instead of PIL
import numpy as np

import gradio as gr
import numpy as np
# from PIL import Image, ImageOps

# Load the model
model = load_model("keras_model.h5", compile=False)

# Load the labels
class_names = open("labels.txt", "r",encoding="utf-8").readlines()

def greet(img):
    data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)

    image = Image.fromarray(img).convert('RGB')
    size = (224, 224)
    image = ImageOps.fit(image, size, Image.ANTIALIAS)

    image_array = np.asarray(image)
    normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
    data[0] = normalized_image_array

    prediction = model.predict(data)

    max_index = np.argmax(prediction) # 確率が一番高いインデクスを抽出

    class_name = class_names[max_index]

    return class_name[2:]

demo = gr.Interface(
    fn=greet,
    inputs=gr.Image(sources=["webcam"], streaming=True),
    outputs="text",
)

# demo.launch(debug=True, share=True)
demo.launch()